


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class        A     (		lowerCamelCase_	):







     '''simple docstring'''







     lowerCamelCase :						List[str]            =   (CMStochasticIterativeScheduler,)
     lowerCamelCase :						List[Any]            =   1_0

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				**_UpperCamelCase    :							Optional[Any]):
      _lowercase:       Any						=				{
          'num_train_timesteps': 201,
          'sigma_min': 0.0_0_2,
          'sigma_max': 80.0,
      }

      config.update(**_UpperCamelCase)
      return config

     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       Optional[Any]						=				10

      _lowercase:       Optional[int]						=				self.get_scheduler_config()
      _lowercase:       Union[str, Any]						=				self.scheduler_classes[0](**_UpperCamelCase)

      scheduler.set_timesteps(_UpperCamelCase)

      _lowercase:       Optional[Any]						=				scheduler.timesteps[0]
      _lowercase:       List[Any]						=				scheduler.timesteps[1]

      _lowercase:       int						=				self.dummy_sample
      _lowercase:       Union[str, Any]						=				0.1 * sample

      _lowercase:       List[str]						=				scheduler.step(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase).prev_sample
      _lowercase:       List[Any]						=				scheduler.step(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase).prev_sample

      self.assertEqual(output_a.shape			,				sample.shape)
      self.assertEqual(output_a.shape			,				output_a.shape)

     def 					UpperCAmelCase__       (				self    :							List[str]):
      for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Dict):
      for clip_denoised in [True, False]:
       self.check_over_configs(clip_denoised=_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      _lowercase:       Tuple						=				self.scheduler_classes[0]
      _lowercase:       Dict						=				self.get_scheduler_config()
      _lowercase:       Optional[int]						=				scheduler_class(**_UpperCamelCase)

      _lowercase:       str						=				1
      scheduler.set_timesteps(_UpperCamelCase)
      _lowercase:       Optional[Any]						=				scheduler.timesteps

      _lowercase:       List[str]						=				torch.manual_seed(0)

      _lowercase:       Dict						=				self.dummy_model()
      _lowercase:       Union[str, Any]						=				self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(_UpperCamelCase):
       # 1. scale model input
       _lowercase:       Optional[int]						=				scheduler.scale_model_input(_UpperCamelCase			,				_UpperCamelCase)

       # 2. predict noise residual
       _lowercase:       List[Any]						=				model(_UpperCamelCase			,				_UpperCamelCase)

       # 3. predict previous sample x_t-1
       _lowercase:       str						=				scheduler.step(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				generator=_UpperCamelCase).prev_sample

       _lowercase:       Optional[Any]						=				pred_prev_sample

      _lowercase:       int						=				torch.sum(torch.abs(_UpperCamelCase))
      _lowercase:       Any						=				torch.mean(torch.abs(_UpperCamelCase))

      assert abs(result_sum.item() - 192.7_614) < 1e-2
      assert abs(result_mean.item() - 0.2_5_1_0) < 1e-3

     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       List[str]						=				self.scheduler_classes[0]
      _lowercase:       int						=				self.get_scheduler_config()
      _lowercase:       Union[str, Any]						=				scheduler_class(**_UpperCamelCase)

      _lowercase:       int						=				[106, 0]
      scheduler.set_timesteps(timesteps=_UpperCamelCase)
      _lowercase:       List[str]						=				scheduler.timesteps

      _lowercase:       List[str]						=				torch.manual_seed(0)

      _lowercase:       Dict						=				self.dummy_model()
      _lowercase:       Any						=				self.dummy_sample_deter * scheduler.init_noise_sigma

      for t in timesteps:
       # 1. scale model input
       _lowercase:       int						=				scheduler.scale_model_input(_UpperCamelCase			,				_UpperCamelCase)

       # 2. predict noise residual
       _lowercase:       int						=				model(_UpperCamelCase			,				_UpperCamelCase)

       # 3. predict previous sample x_t-1
       _lowercase:       str						=				scheduler.step(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				generator=_UpperCamelCase).prev_sample

       _lowercase:       Optional[Any]						=				pred_prev_sample

      _lowercase:       List[Any]						=				torch.sum(torch.abs(_UpperCamelCase))
      _lowercase:       int						=				torch.mean(torch.abs(_UpperCamelCase))

      assert abs(result_sum.item() - 347.6_357) < 1e-2
      assert abs(result_mean.item() - 0.4_5_2_7) < 1e-3

     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      _lowercase:       Dict						=				self.scheduler_classes[0]
      _lowercase:       Optional[int]						=				self.get_scheduler_config()
      _lowercase:       str						=				scheduler_class(**_UpperCamelCase)

      _lowercase:       Any						=				[39, 30, 12, 15, 0]

      with self.assertRaises(_UpperCamelCase			,				msg="`timesteps` must be in descending order."):
       scheduler.set_timesteps(timesteps=_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      _lowercase:       Tuple						=				self.scheduler_classes[0]
      _lowercase:       Union[str, Any]						=				self.get_scheduler_config()
      _lowercase:       Any						=				scheduler_class(**_UpperCamelCase)

      _lowercase:       Tuple						=				[39, 30, 12, 1, 0]
      _lowercase:       str						=				len(_UpperCamelCase)

      with self.assertRaises(_UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."):
       scheduler.set_timesteps(num_inference_steps=_UpperCamelCase			,				timesteps=_UpperCamelCase)






     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       int						=				self.scheduler_classes[0]
      _lowercase:       List[str]						=				self.get_scheduler_config()
      _lowercase:       List[Any]						=				scheduler_class(**_UpperCamelCase)

      _lowercase:       Union[str, Any]						=				[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          _UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
       scheduler.set_timesteps(timesteps=_UpperCamelCase)









"""simple docstring"""


from math import sqrt


def        UpperCAmelCase      (					a__ = 1_00_00_00 ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int						=			0
						lowerCAmelCase   :int

						while num_cuboids <= limit:
												max_cuboid_size += 1
												for sum_shortest_sides in range(2     ,      2 * max_cuboid_size + 1 ):
																		if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
																								num_cuboids += (
																								    min(a__     ,      sum_shortest_sides // 2 )
																								    - max(1     ,      sum_shortest_sides - max_cuboid_size )
																								    + 1
																								)

						return max_cuboid_size


if __name__ == "__main__":
		print(F"""{solution() = }""")